Graphical Abstract:

Abstract:

Background: Alzheimer Disease (AD) represents a major threat to the lives of human
beings. In fact, the disease should be detected at an early stage to maximize the chances of survival.

Matarial and Methods: Hence, the use of computer means making the diagnostic procedure automatic
called: Computer-Assisted Diagnosis (CAD). This procedure is used to assist radiologists in
the analysis of the disease; the number of the affected persons continues to grow in recent decades.
As to our work, we made a Computer-Assisted Diagnosis for detecting Alzheimer's disease in early
step Mild Cognitive Impairment (MCI).

Conclusion: Our system contains three parts: Preprocessing, segmentation and a classification step. For
the pretreatment step we used the Non-Local Means Filter (NLMF), the deformable model Level Set in
the segmentation step to extract the Cortex and Hippocampus. Our contribution is to improve the
segmentation step: we determined a priori shape and an automatic position for the initialization. Also,
we added a priori knowledge of the surface. For the classification, our method is based on Support Vector
Machine (SVM). The proposed system yields 92.5% accuracy in the early diagnosis of the AD.

Abstract:Background: Alzheimer Disease (AD) represents a major threat to the lives of human
beings. In fact, the disease should be detected at an early stage to maximize the chances of survival.

Matarial and Methods: Hence, the use of computer means making the diagnostic procedure automatic
called: Computer-Assisted Diagnosis (CAD). This procedure is used to assist radiologists in
the analysis of the disease; the number of the affected persons continues to grow in recent decades.
As to our work, we made a Computer-Assisted Diagnosis for detecting Alzheimer's disease in early
step Mild Cognitive Impairment (MCI).

Conclusion: Our system contains three parts: Preprocessing, segmentation and a classification step. For
the pretreatment step we used the Non-Local Means Filter (NLMF), the deformable model Level Set in
the segmentation step to extract the Cortex and Hippocampus. Our contribution is to improve the
segmentation step: we determined a priori shape and an automatic position for the initialization. Also,
we added a priori knowledge of the surface. For the classification, our method is based on Support Vector
Machine (SVM). The proposed system yields 92.5% accuracy in the early diagnosis of the AD.